When a neural network is applied to complicated tasks, e.g. image classification and object recognition in images, many inputs, outputs, and deeper hidden layers may be required. This type of network is called a deep neural network (DNN), and the application of this model is called DL [1]}, [2]}. In the last few decades, dramatic advances have been achieved in DNN, especially in image recognition fields, owing to the development of the convolutional neural network (CNN) [3]}. CNN is a technique that compacts information and is composed of convolution layers and pooling layers (Fig. REF ) [4]}. The characteristic idea of CNN is that of a filtering technique. CNN slices input image data using a filter, e.g. a 3 × 3 filter, and performs calculations with the sliced data. Through this procedure, the input data are condensed in stages to extract noteworthy information. The first hidden layers react to simple features such as edges and blobs, the middle layers react to patterns and texture, and the last layers react to object parts. The output layer considers the combinations of object parts and classifies the input data into object classes [4]}. In recent years, to improve the capability of DNN and its scope of application, many techniques and network structures have been proposed, such as the recurrent neural network (RNN), long-short term memory (LSTM), and attention [6]}, for speech and audio processing, natural language processing, information retrieval, object recognition, segmentation, and classification [2]}.
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